{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 安装类库\n",
    "# !mkdir /home/aistudio/external-libraries\n",
    "# !pip install imgaug -t /home/aistudio/external-libraries\n",
    "import sys\n",
    "sys.path.append('/home/aistudio/external-libraries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-01-14 09:30:17,099-INFO: font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']\n",
      "2021-01-14 09:30:17,441-INFO: generated new fontManager\n",
      "Cache file /home/aistudio/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz not found, downloading https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz \n",
      "Begin to download\n",
      "\n",
      "Download finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: (32, 32, 3)\n",
      "label value: horse\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import imgaug as ia\n",
    "import imgaug.augmenters as iaa\n",
    "\n",
    "# 读取数据\n",
    "reader = paddle.batch(\n",
    "    paddle.dataset.cifar.train10(),\n",
    "    batch_size=8) # 数据集读取器\n",
    "data = next(reader()) # 读取数据\n",
    "index = 4 # 批次索引\n",
    "\n",
    "# 读取图像\n",
    "image = np.array([x[0] for x in data]).astype(np.float32) # 读取图像数据，数据类型为float32\n",
    "image = image * 255 # 从[0,1]转换到[0,255]\n",
    "image = image[index].reshape((3, 32, 32)).transpose((1, 2, 0)).astype(np.uint8) # 数据格式从CHW转换为HWC，数据类型转换为uint8\n",
    "print('image shape:', image.shape)\n",
    "\n",
    "# 图像增强\n",
    "# sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 随机进行图像增强\n",
    "# seq = iaa.Sequential([\n",
    "#     sometimes(iaa.CropAndPad(px=(-4, 4))),      # 随机裁剪填充像素\n",
    "#     iaa.Fliplr(0.5)])                           # 随机进行水平翻转\n",
    "# image = seq(image=image)\n",
    "\n",
    "# 读取标签\n",
    "label = np.array([x[1] for x in data]).astype(np.int64) # 读取标签数据，数据类型为int64\n",
    "vlist = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"] # 标签名称列表\n",
    "print('label value:', vlist[label[index]])\n",
    "\n",
    "# 显示图像\n",
    "image = Image.fromarray(image)   # 转换图像格式\n",
    "image.save('./work/out/img.png') # 保存读取图像\n",
    "plt.figure(figsize=(3, 3))       # 设置显示大小\n",
    "plt.imshow(image)                # 设置显示图像\n",
    "plt.show()                       # 显示图像文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data: image shape (128, 3, 32, 32), label shape:(128, 1)\n",
      "valid_data: image shape (128, 3, 32, 32), label shape:(128, 1)\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "import imgaug as ia\n",
    "import imgaug.augmenters as iaa\n",
    "\n",
    "# 训练数据增强\n",
    "def train_augment(images):\n",
    "    # 转换格式\n",
    "    images = images * 255 # 从[0,1]转换到[0,255]\n",
    "    images = images.transpose((0, 2, 3, 1)).astype(np.uint8) # 数据格式从BCHW转换为BHWC，数据类型转换为uint8\n",
    "    \n",
    "    # 增强图像\n",
    "    sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 随机进行图像增强\n",
    "    seq = iaa.Sequential([\n",
    "        sometimes(iaa.CropAndPad(px=(-4, 4))),      # 随机裁剪填充像素\n",
    "        iaa.Fliplr(0.5)])                           # 随机进行水平翻转\n",
    "    images = seq(images=images)\n",
    "    \n",
    "    # 减去均值\n",
    "    mean = np.array([0.4914, 0.4822, 0.4465]).reshape((1, 1, 1, -1)) # cifar数据集通道平均值\n",
    "    stdv = np.array([0.2471, 0.2435, 0.2616]).reshape((1, 1, 1, -1)) # cifar数据集通道标准差\n",
    "    \n",
    "    images = (images/255.0 - mean) / stdv # 对图像进行归一化\n",
    "    images = images.transpose((0, 3, 1, 2)).astype(np.float32) # 数据格式从BHWC转换为BCHW，数据类型转换为float32\n",
    "    \n",
    "    return images\n",
    "\n",
    "# 验证数据增强\n",
    "def valid_augment(images):\n",
    "    # 转换格式\n",
    "    images = images * 255 # 从[0,1]转换到[0,255]\n",
    "    images = images.transpose((0, 2, 3, 1)).astype(np.uint8) # 数据格式从BCHW转换为BHWC，数据类型转换为uint8\n",
    "    \n",
    "    # 减去均值\n",
    "    mean = np.array([0.4914, 0.4822, 0.4465]).reshape((1, 1, 1, -1)) # cifar数据集通道平均值\n",
    "    stdv = np.array([0.2471, 0.2435, 0.2616]).reshape((1, 1, 1, -1)) # cifar数据集通道标准差\n",
    "    \n",
    "    images = (images/255.0 - mean) / stdv # 对图像进行归一化\n",
    "    images = images.transpose((0, 3, 1, 2)).astype(np.float32) # 数据格式从BHWC转换为BCHW，数据类型转换为float32\n",
    "    \n",
    "    return images\n",
    "\n",
    "# 读取训练数据\n",
    "train_reader = paddle.batch(\n",
    "    paddle.reader.shuffle(paddle.dataset.cifar.train10(), buf_size=50000),\n",
    "    batch_size=128) # 构造数据读取器\n",
    "train_data = next(train_reader()) # 读取训练数据\n",
    "\n",
    "train_image = np.array([x[0] for x in train_data]).reshape((-1, 3, 32, 32)).astype(np.float32) # 读取训练图像\n",
    "train_image = train_augment(train_image)                                                       # 训练图像增强\n",
    "train_label = np.array([x[1] for x in train_data]).reshape((-1, 1)).astype(np.int64)           # 读取训练标签\n",
    "print('train_data: image shape {}, label shape:{}'.format(train_image.shape, train_label.shape))\n",
    "\n",
    "# 读取验证数据\n",
    "valid_reader = paddle.batch(\n",
    "    paddle.dataset.cifar.test10(),\n",
    "    batch_size=128) # 构造数据读取器\n",
    "valid_data = next(valid_reader()) # 读取验证数据\n",
    "\n",
    "valid_image = np.array([x[0] for x in valid_data]).reshape((-1, 3, 32, 32)).astype(np.float32) # 读取验证图像\n",
    "valid_image = valid_augment(valid_image)                                                       # 验证图像增强\n",
    "valid_label = np.array([x[1] for x in valid_data]).reshape((-1, 1)).astype(np.int64)           # 读取验证标签\n",
    "print('valid_data: image shape {}, label shape:{}'.format(valid_image.shape, valid_label.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 模型设计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle.fluid as fluid\n",
    "from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear, BatchNorm\n",
    "import math\n",
    "\n",
    "# 模组结构：输入维度，输出维度，滑动步长，基础长度, 队列长度\n",
    "group_arch = [(3, 256, 1, 2, 1), (256, 256, 2, 2, 1), (256, 256, 2, 2, 1)]\n",
    "group_dim  = 256 # 模组输出维度\n",
    "class_dim  = 10  # 类别数量维度\n",
    "\n",
    "# 卷积单元\n",
    "class ConvUnit(fluid.dygraph.Layer):\n",
    "    def __init__(self, in_dim, out_dim, filter_size=3, stride=1, act=None):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化卷积单元，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "            in_dim      - 输入维度\n",
    "            out_dim     - 输出维度\n",
    "            filter_size - 卷积大小\n",
    "            stride      - 滑动步长\n",
    "            act         - 激活函数\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(ConvUnit, self).__init__()\n",
    "        \n",
    "        # 添加卷积\n",
    "        self.conv = Conv2D(\n",
    "            num_channels=in_dim,\n",
    "            num_filters=out_dim,\n",
    "            filter_size=filter_size,\n",
    "            stride=stride,\n",
    "            padding=(filter_size-1)//2,                       # 输出特征图大小不变\n",
    "            param_attr=fluid.initializer.MSRA(uniform=False), # 使用MARA 初始权重\n",
    "            bias_attr=False,                                  # 卷积输出没有偏置项\n",
    "            act=None)\n",
    "        \n",
    "        # 添加正则\n",
    "        self.norm = BatchNorm(\n",
    "            num_channels=out_dim,\n",
    "            param_attr=fluid.initializer.Constant(1.0), # 使用常量初始化权重\n",
    "            bias_attr=fluid.initializer.Constant(0.0),  # 使用常量初始化偏置\n",
    "            act=act)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征进行卷积和正则\n",
    "        输入:\n",
    "            x - 输入特征\n",
    "        输出:\n",
    "            x - 输出特征\n",
    "        \"\"\"\n",
    "        # 进行卷积\n",
    "        x = self.conv(x)\n",
    "        \n",
    "        # 进行正则\n",
    "        x = self.norm(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "# 投影单元\n",
    "class ProjUnit(fluid.dygraph.Layer):\n",
    "    def __init__(self, in_dim, out_dim, filter_size=1, stride=1, act=None):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化投影单元，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "            in_dim      - 输入维度\n",
    "            out_dim     - 输出维度\n",
    "            filter_size - 卷积大小\n",
    "            stride      - 滑动步长\n",
    "            act         - 激活函数\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(ProjUnit, self).__init__()\n",
    "        \n",
    "        # 添加池化\n",
    "        self.pool = Pool2D(\n",
    "            pool_size=filter_size,\n",
    "            pool_stride=stride,\n",
    "            pool_padding=0,\n",
    "            pool_type='avg')\n",
    "        \n",
    "        # 添加卷积\n",
    "        self.conv = Conv2D(\n",
    "            num_channels=in_dim,\n",
    "            num_filters=out_dim,\n",
    "            filter_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            param_attr=fluid.initializer.MSRA(uniform=False), # 使用MARA 初始权重\n",
    "            bias_attr=False,                                  # 卷积输出没有偏置项\n",
    "            act=None)\n",
    "        \n",
    "        # 添加正则\n",
    "        self.norm = BatchNorm(\n",
    "            num_channels=out_dim,\n",
    "            param_attr=fluid.initializer.Constant(1.0), # 使用常量初始化权重\n",
    "            bias_attr=fluid.initializer.Constant(0.0),  # 使用常量初始化偏置\n",
    "            act=act)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征进行池化卷积和正则\n",
    "        输入:\n",
    "            x - 输入特征\n",
    "        输出:\n",
    "            x - 输出特征\n",
    "        \"\"\"\n",
    "        # 进行池化\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        # 进行卷积\n",
    "        x = self.conv(x)\n",
    "        \n",
    "        # 进行正则\n",
    "        x = self.norm(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "# 队列结构\n",
    "class SSRQueue(fluid.dygraph.Layer):\n",
    "    def __init__(self, in_dim, out_dim, stride=1, queues=2, act=None):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化队列结构，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "            in_dim  - 输入维度\n",
    "            out_dim - 输出维度\n",
    "            stride  - 滑动步长，1保持不变，2下采样\n",
    "            queues  - 队列长度，分割尺度为2^(n-1)\n",
    "            act     - 激活函数\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(SSRQueue, self).__init__()\n",
    "        \n",
    "        # 添加队列变量\n",
    "        self.queues = queues # 队列长度\n",
    "        self.split_list = [] # 分割列表\n",
    "        \n",
    "        # 添加队列列表\n",
    "        self.queue_list = [] # 队列列表\n",
    "        for i in range(queues):\n",
    "            # 添加队列项目\n",
    "            queue_item = self.add_sublayer( # 构造队列项目\n",
    "                'queue_' + str(i),\n",
    "                ConvUnit(\n",
    "                    in_dim=(in_dim if i==0 else out_dim), # 每组队列项目除第一个外，in_dim=out_dim\n",
    "                    out_dim=out_dim,\n",
    "                    filter_size=3,\n",
    "                    stride=(stride if i==0 else 1), # 每组队列项目除第一块外，stride=1\n",
    "                    act=act))\n",
    "            self.queue_list.append(queue_item) # 添加队列项目\n",
    "            \n",
    "            # 计算输出维度\n",
    "            if i < (queues-1): # 如果不是最后一项\n",
    "                out_dim = out_dim//2 # 输出维度减半\n",
    "                self.split_list.append(out_dim) # 添加分割列表\n",
    "            \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征图像提取特征\n",
    "        输入:\n",
    "            x - 输入特征\n",
    "        输出:\n",
    "            x - 输出特征\n",
    "        \"\"\"\n",
    "        # 提取特征\n",
    "        x_list = [] # 队列输出列表\n",
    "        for i, queue_item in enumerate(self.queue_list):\n",
    "            if i < (self.queues-1): # 如果不是最后一项\n",
    "                x = queue_item(x) # 提取队列特征\n",
    "                x_item, x = fluid.layers.split(input=x, num_or_sections=[-1, self.split_list[i]], dim=1)\n",
    "                x_list.append(x_item) # 添加输出列表\n",
    "            else: # 否则不对特征分割\n",
    "                x = queue_item(x) # 提取队列特征\n",
    "                x_list.append(x) # 添加输出列表\n",
    "        \n",
    "        # 联结特征\n",
    "        x = fluid.layers.concat(input=x_list, axis=1) # 队列输出列表按通道维进行特征联结\n",
    "        \n",
    "        return x\n",
    "    \n",
    "# 基础结构\n",
    "class SSRBasic(fluid.dygraph.Layer):\n",
    "    def __init__(self, in_dim, out_dim, stride=1, queues=1, is_pass=True):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化基础结构，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "            in_dim  - 输入维度\n",
    "            out_dim - 输出维度\n",
    "            stride  - 滑动步长\n",
    "            queues  - 队列长度\n",
    "            is_pass - 是否直连\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(SSRBasic, self).__init__()\n",
    "        \n",
    "        # 是否直连标识\n",
    "        self.is_pass = is_pass\n",
    "        \n",
    "        # 添加投影路径\n",
    "        self.proj = ProjUnit(in_dim=in_dim, out_dim=out_dim, filter_size=stride, stride=stride, act=None)\n",
    "        \n",
    "        # 添加卷积路径\n",
    "        if queues==1:\n",
    "            self.conv = ConvUnit(in_dim=in_dim, out_dim=out_dim, filter_size=3, stride=stride, act='relu')\n",
    "        else:\n",
    "            self.conv = SSRQueue(in_dim=in_dim, out_dim=out_dim, stride=stride, queues=queues, act='relu')\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征图像提取特征\n",
    "        输入:\n",
    "            x - 输入特征\n",
    "        输出:\n",
    "            x - 输出特征\n",
    "            y - 输出特征\n",
    "        \"\"\"\n",
    "        # 直连路径\n",
    "        if self.is_pass: # 是否直连\n",
    "            x_pass = x\n",
    "        else:            # 否则投影\n",
    "            x_pass = self.proj(x)\n",
    "        \n",
    "        # 卷积路径\n",
    "        x_conv = self.conv(x)\n",
    "        \n",
    "        # 输出特征\n",
    "        x = fluid.layers.elementwise_add(x=x_pass, y=x_conv, act=None) # 直连路径与卷积路径进行特征相加\n",
    "        y = x\n",
    "        \n",
    "        return x, y\n",
    "    \n",
    "# 模块结构\n",
    "class SSRBlock(fluid.dygraph.Layer):\n",
    "    def __init__(self, in_dim, out_dim, stride=1, basics=1, queues=1):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化模块结构，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "            in_dim  - 输入维度\n",
    "            out_dim - 输出维度\n",
    "            stride  - 滑动步长\n",
    "            basics  - 基础长度\n",
    "            queues  - 队列长度\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(SSRBlock, self).__init__()\n",
    "        \n",
    "        # 添加模块列表\n",
    "        self.block_list = [] # 模块列表\n",
    "        for i in range(basics):\n",
    "            block_item = self.add_sublayer( # 构造模块项目\n",
    "                'block_' + str(i),\n",
    "                SSRBasic(\n",
    "                    in_dim=(in_dim if i==0 else out_dim), # 每组模块项目除第一块外，输入维度=输出维度\n",
    "                    out_dim=out_dim,\n",
    "                    stride=(stride if i==0 else 1), # 每组模块项目除第一块外，stride=1\n",
    "                    queues=queues,\n",
    "                    is_pass=(False if i==0 else True))) # 每组模块项目除第一块外，is_pass=True\n",
    "            self.block_list.append(block_item) # 添加模块项目\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征图像提取特征\n",
    "        输入:\n",
    "            x      - 输入特征\n",
    "        输出:\n",
    "            x      - 输出特征\n",
    "            y_list - 输出特征列表\n",
    "        \"\"\"\n",
    "        y_list = [] # 模块输出列表\n",
    "        for block_item in self.block_list:\n",
    "            x, y_item = block_item(x) # 提取模块特征\n",
    "            y_list.append(y_item) # 添加输出列表\n",
    "            \n",
    "        return x, y_list\n",
    "\n",
    "# 模组结构\n",
    "class SSRGroup(fluid.dygraph.Layer):\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化模组结构，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(SSRGroup, self).__init__()\n",
    "        \n",
    "        # 添加模组列表\n",
    "        self.group_list = [] # 模组列表\n",
    "        for i, block_arch in enumerate(group_arch):\n",
    "            group_item = self.add_sublayer( # 构造模组项目\n",
    "                'group_' + str(i),\n",
    "                SSRBlock(\n",
    "                    in_dim=block_arch[0],\n",
    "                    out_dim=block_arch[1],\n",
    "                    stride=block_arch[2],\n",
    "                    basics=block_arch[3],\n",
    "                    queues=block_arch[4]))\n",
    "            self.group_list.append(group_item) # 添加模组项目\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入的特征图像提取特征\n",
    "        输入:\n",
    "            x      - 输入特征\n",
    "        输出:\n",
    "            x      - 输出特征\n",
    "            y_list - 输出特征列表\n",
    "        \"\"\"\n",
    "        y_list = [] # 模组输出列表\n",
    "        for group_item in self.group_list:\n",
    "            x, y_item = group_item(x) # 提取模组特征\n",
    "            y_list.append(y_item) # 添加输出列表\n",
    "            \n",
    "        return x, y_list\n",
    "        \n",
    "# 分割网络\n",
    "class SSRNet(fluid.dygraph.Layer):\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            初始化分割网络，H/W=(H/W+2*P-F)/S+1\n",
    "        输入:\n",
    "        输出:\n",
    "        \"\"\"\n",
    "        super(SSRNet, self).__init__()\n",
    "        \n",
    "        # 添加模组结构\n",
    "        self.backbone = SSRGroup() # 输出：N*C*H*W\n",
    "        \n",
    "        # 添加全连接层\n",
    "        self.pool = Pool2D(global_pooling=True, pool_type='avg') # 输出：N*C*1*1\n",
    "        \n",
    "        stdv = 1.0/(math.sqrt(group_dim)*1.0)                    # 设置均匀分布权重方差\n",
    "        self.fc = Linear(                                        # 输出：=N*10\n",
    "            input_dim=group_dim,\n",
    "            output_dim=class_dim,\n",
    "            param_attr=fluid.initializer.Uniform(-stdv, stdv),   # 使用均匀分布初始权重\n",
    "            bias_attr=fluid.initializer.Constant(0.0),           # 使用常量数值初始偏置\n",
    "            act='softmax')\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        功能:\n",
    "            对输入图像进行分类\n",
    "        输入:\n",
    "            x - 输入图像\n",
    "        输出:\n",
    "            x - 预测结果\n",
    "        \"\"\"\n",
    "        # 提取特征\n",
    "        x, y_list = self.backbone(x)\n",
    "        \n",
    "        # 进行预测\n",
    "        x = self.pool(x)\n",
    "        x = fluid.layers.reshape(x, [x.shape[0], -1])\n",
    "        x = self.fc(x)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tatol param: 3299338\n",
      "infer shape: [1, 10]\n"
     ]
    }
   ],
   "source": [
    "import paddle.fluid as fluid\n",
    "from paddle.fluid.dygraph.base import to_variable\n",
    "import numpy as np\n",
    "\n",
    "with fluid.dygraph.guard():\n",
    "    # 输入数据\n",
    "    x = np.random.randn(1, 3, 32, 32).astype(np.float32)\n",
    "    x = to_variable(x)\n",
    "    \n",
    "    # 进行预测\n",
    "    backbone = SSRNet() # 设置网络\n",
    "    \n",
    "    infer = backbone(x) # 进行预测\n",
    "    \n",
    "    # 显示结果\n",
    "    parameters = 0\n",
    "    for p in backbone.parameters():\n",
    "        parameters += np.prod(p.shape) # 统计参数\n",
    "    \n",
    "    print('tatol param:', parameters)\n",
    "    print('infer shape:', infer.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complete - train time: 9411s, best epoch: 218, best loss: 0.293680, best accuracy: 92.93%\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\n",
    "import paddle.fluid as fluid\n",
    "from paddle.utils.plot import Ploter\n",
    "import numpy as np\n",
    "import time\n",
    "import math\n",
    "import os\n",
    "\n",
    "epoch_num = 300   # 训练周期，取值一般为[1,300]\n",
    "train_batch = 128 # 训练批次，取值一般为[1,256]\n",
    "valid_batch = 128 # 验证批次，取值一般为[1,256]\n",
    "displays = 100    # 显示迭代\n",
    "\n",
    "start_lr = 0.00001                         # 开始学习率，取值一般为[1e-8,5e-1]\n",
    "based_lr = 0.1                             # 基础学习率，取值一般为[1e-8,5e-1]\n",
    "epoch_iters = math.ceil(50000/train_batch) # 每轮迭代数\n",
    "warmup_iter = 10 * epoch_iters             # 预热迭代数，取值一般为[1,10]\n",
    "\n",
    "momentum = 0.9     # 优化器动量\n",
    "l2_decay = 0.00005 # 正则化系数，取值一般为[1e-5,5e-4]\n",
    "epsilon = 0.05     # 标签平滑率，取值一般为[1e-2,1e-1]\n",
    "\n",
    "checkpoint = False                   # 断点标识\n",
    "model_path = './work/out/ssrnet'     # 模型路径\n",
    "result_txt = './work/out/result.txt' # 结果文件\n",
    "class_num  = 10                      # 类别数量\n",
    "\n",
    "with fluid.dygraph.guard():\n",
    "    # 准备数据\n",
    "    train_reader = paddle.batch(\n",
    "        reader=paddle.reader.shuffle(reader=paddle.dataset.cifar.train10(), buf_size=50000),\n",
    "        batch_size=train_batch)\n",
    "    \n",
    "    valid_reader = paddle.batch(\n",
    "        reader=paddle.dataset.cifar.test10(),\n",
    "        batch_size=valid_batch)\n",
    "    \n",
    "    # 声明模型\n",
    "    model = SSRNet()\n",
    "    \n",
    "    # 优化算法\n",
    "    consine_lr = fluid.layers.cosine_decay(based_lr, epoch_iters, epoch_num) # 余弦衰减策略\n",
    "    decayed_lr = fluid.layers.linear_lr_warmup(consine_lr, warmup_iter, start_lr, based_lr) # 线性预热策略\n",
    "    \n",
    "    optimizer = fluid.optimizer.Momentum(\n",
    "        learning_rate=decayed_lr,                           # 衰减学习策略\n",
    "        momentum=momentum,                                  # 优化动量系数\n",
    "        regularization=fluid.regularizer.L2Decay(l2_decay), # 正则衰减系数\n",
    "        parameter_list=model.parameters())\n",
    "    \n",
    "    # 加载断点\n",
    "    if checkpoint: # 是否加载断点文件\n",
    "        model_dict, optimizer_dict = fluid.load_dygraph(model_path) # 加载断点参数\n",
    "        model.set_dict(model_dict)                                  # 设置权重参数\n",
    "        optimizer.set_dict(optimizer_dict)                          # 设置优化参数\n",
    "    else:          # 否则删除结果文件\n",
    "        if os.path.exists(result_txt): # 如果存在结果文件\n",
    "            os.remove(result_txt)      # 那么删除结果文件\n",
    "    \n",
    "    # 初始训练\n",
    "    avg_train_loss = 0 # 平均训练损失\n",
    "    avg_valid_loss = 0 # 平均验证损失\n",
    "    avg_valid_accu = 0 # 平均验证精度\n",
    "    \n",
    "    iterator = 1                                # 迭代次数\n",
    "    train_prompt = \"Train loss\"                 # 训练标签\n",
    "    valid_prompt = \"Valid loss\"                 # 验证标签\n",
    "    ploter = Ploter(train_prompt, valid_prompt) # 训练图像\n",
    "    \n",
    "    best_epoch = 0           # 最好周期\n",
    "    best_accu = 0            # 最好精度\n",
    "    best_loss = 100.0        # 最好损失\n",
    "    train_time = time.time() # 训练时间\n",
    "    \n",
    "    # 开始训练\n",
    "    for epoch_id in range(epoch_num):\n",
    "        # 训练模型\n",
    "        model.train() # 设置训练\n",
    "        for batch_id, train_data in enumerate(train_reader()):\n",
    "            # 读取数据\n",
    "            image_data = np.array([x[0] for x in train_data]).reshape((-1, 3, 32, 32)).astype(np.float32) # 读取图像数据\n",
    "            image_data = train_augment(image_data)                                                        # 使用数据增强\n",
    "            image = fluid.dygraph.to_variable(image_data)                                                 # 转换数据类型\n",
    "\n",
    "            label_data = np.array([x[1] for x in train_data]).astype(np.int64)                        # 读取标签数据\n",
    "            label = fluid.dygraph.to_variable(label_data)                                             # 转换数据类型\n",
    "            label = fluid.layers.label_smooth(label=fluid.one_hot(label, class_num), epsilon=epsilon) # 使用标签平滑\n",
    "            label.stop_gradient = True                                                                # 停止梯度传播\n",
    "\n",
    "            # 前向传播\n",
    "            infer = model(image)\n",
    "            \n",
    "            # 计算损失\n",
    "            loss = fluid.layers.cross_entropy(infer, label, soft_label=True)\n",
    "            train_loss = fluid.layers.mean(loss)\n",
    "            \n",
    "            # 反向传播\n",
    "            train_loss.backward()\n",
    "            optimizer.minimize(train_loss)\n",
    "            model.clear_gradients()\n",
    "            \n",
    "            # 显示结果\n",
    "            if iterator % displays == 0:\n",
    "                # 显示图像\n",
    "                avg_train_loss = train_loss.numpy()[0]                # 设置训练损失\n",
    "                ploter.append(train_prompt, iterator, avg_train_loss) # 添加训练图像\n",
    "                ploter.plot()                                         # 显示训练图像\n",
    "                \n",
    "                # 打印结果\n",
    "                print(\"iteration: {:6d}, epoch: {:3d}, train loss: {:.6f}, valid loss: {:.6f}, valid accuracy: {:.2%}\".format(\n",
    "                    iterator, epoch_id+1, avg_train_loss, avg_valid_loss, avg_valid_accu))\n",
    "                \n",
    "                # 写入文件\n",
    "                with open(result_txt, 'a') as file:\n",
    "                    file.write(\"iteration: {:6d}, epoch: {:3d}, train loss: {:.6f}, valid loss: {:.6f}, valid accuracy: {:.2%}\\n\".format(\n",
    "                        iterator, epoch_id+1, avg_train_loss, avg_valid_loss, avg_valid_accu))\n",
    "            \n",
    "            # 增加迭代\n",
    "            iterator += 1\n",
    "            \n",
    "        # 验证模型\n",
    "        valid_loss_list = [] # 验证损失列表\n",
    "        valid_accu_list = [] # 验证精度列表\n",
    "        \n",
    "        model.eval()   # 设置验证\n",
    "        for batch_id, valid_data in enumerate(valid_reader()):\n",
    "            # 读取数据\n",
    "            image_data = np.array([x[0] for x in valid_data]).reshape((-1, 3, 32, 32)).astype(np.float32) # 读取图像数据\n",
    "            image_data = valid_augment(image_data)                                                        # 使用图像增强\n",
    "            image = fluid.dygraph.to_variable(image_data)                                                 # 转换数据类型\n",
    "            \n",
    "            label_data = np.array([x[1] for x in valid_data]).reshape((-1, 1)).astype(np.int64) # 读取标签数据\n",
    "            label = fluid.dygraph.to_variable(label_data)                                       # 转换数据类型\n",
    "            label.stop_gradient = True                                                          # 停止梯度传播\n",
    "            \n",
    "            # 前向传播\n",
    "            infer = model(image)\n",
    "            \n",
    "            # 计算精度\n",
    "            valid_accu = fluid.layers.accuracy(infer,label)\n",
    "            \n",
    "            valid_accu_list.append(valid_accu.numpy())\n",
    "            \n",
    "            # 计算损失\n",
    "            loss = fluid.layers.cross_entropy(infer, label)\n",
    "            valid_loss = fluid.layers.mean(loss)\n",
    "            \n",
    "            valid_loss_list.append(valid_loss.numpy())\n",
    "        \n",
    "        # 设置结果\n",
    "        avg_valid_accu = np.mean(valid_accu_list)             # 设置验证精度\n",
    "        \n",
    "        avg_valid_loss = np.mean(valid_loss_list)             # 设置验证损失\n",
    "        ploter.append(valid_prompt, iterator, avg_valid_loss) # 添加训练图像\n",
    "        \n",
    "        # 保存模型\n",
    "        fluid.save_dygraph(model.state_dict(), model_path)     # 保存权重参数\n",
    "        fluid.save_dygraph(optimizer.state_dict(), model_path) # 保存优化参数\n",
    "        \n",
    "        if avg_valid_loss < best_loss:\n",
    "            fluid.save_dygraph(model.state_dict(), model_path + '-best') # 保存权重\n",
    "            \n",
    "            best_epoch = epoch_id + 1                                    # 更新迭代\n",
    "            best_accu = avg_valid_accu                                   # 更新精度\n",
    "            best_loss = avg_valid_loss                                   # 更新损失\n",
    "    \n",
    "    # 显示结果\n",
    "    train_time = time.time() - train_time # 设置训练时间\n",
    "    print('complete - train time: {:.0f}s, best epoch: {:3d}, best loss: {:.6f}, best accuracy: {:.2%}'.format(\n",
    "        train_time, best_epoch, best_loss, best_accu))\n",
    "    \n",
    "    # 写入文件\n",
    "    with open(result_txt, 'a') as file:\n",
    "        file.write('complete - train time: {:.0f}s, best epoch: {:3d}, best loss: {:.6f}, best accuracy: {:.2%}\\n'.format(\n",
    "            train_time, best_epoch, best_loss, best_accu))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "infer time: 0.004964s, infer value: horse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle.fluid as fluid\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image_path = './work/out/img.png' # 图片路径\n",
    "model_path = './work/out/ssrnet-best' # 模型路径\n",
    "\n",
    "# 加载图像\n",
    "def load_image(image_path):\n",
    "    \"\"\"\n",
    "    功能:\n",
    "        读取图像并转换到输入格式\n",
    "    输入:\n",
    "        image_path - 输入图像路径\n",
    "    输出:\n",
    "        image - 输出图像\n",
    "    \"\"\"\n",
    "    # 读取图像\n",
    "    image = Image.open(image_path) # 打开图像文件\n",
    "    \n",
    "    # 转换格式\n",
    "    image = image.resize((32, 32), Image.ANTIALIAS) # 调整图像大小\n",
    "    image = np.array(image, dtype=np.float32) # 转换数据格式，数据类型转换为float32\n",
    "\n",
    "    # 减去均值\n",
    "    mean = np.array([0.4914, 0.4822, 0.4465]).reshape((1, 1, -1)) # cifar数据集通道平均值\n",
    "    stdv = np.array([0.2471, 0.2435, 0.2616]).reshape((1, 1, -1)) # cifar数据集通道标准差\n",
    "    \n",
    "    image = (image/255.0 - mean) / stdv # 对图像进行归一化\n",
    "    image = image.transpose((2, 0, 1)).astype(np.float32) # 数据格式从HWC转换为CHW，数据类型转换为float32\n",
    "    \n",
    "    # 增加维度\n",
    "    image = np.expand_dims(image, axis=0) # 增加数据维度\n",
    "    \n",
    "    return image\n",
    "\n",
    "# 预测图像\n",
    "with fluid.dygraph.guard():\n",
    "    # 读取图像\n",
    "    image = load_image(image_path)\n",
    "    image = fluid.dygraph.to_variable(image)\n",
    "    \n",
    "    # 加载模型\n",
    "    model = SSRNet()                               # 加载模型\n",
    "    model_dict, _ = fluid.load_dygraph(model_path) # 加载权重\n",
    "    model.set_dict(model_dict)                     # 设置权重\n",
    "    model.eval()                                   # 设置验证\n",
    "    \n",
    "    # 前向传播\n",
    "    infer_time = time.time()              # 推断开始时间\n",
    "    infer = model(image)\n",
    "    infer_time = time.time() - infer_time # 推断结束时间\n",
    "    \n",
    "    # 显示结果\n",
    "    vlist = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"] # 标签名称列表\n",
    "    print('infer time: {:f}s, infer value: {}'.format(infer_time, vlist[np.argmax(infer.numpy())]) )\n",
    "    \n",
    "    image = Image.open(image_path) # 打开图像文件\n",
    "    plt.figure(figsize=(3, 3))     # 设置显示大小\n",
    "    plt.imshow(image)              # 设置显示图像\n",
    "    plt.show()                     # 显示图像文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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